Orbán G., Lengyel M. & Fiser J. (2007) Spontaneous activity in V1: a probabilistic framework. Sloan-Swartz Meeting of Theoretical Neurobiology 2007, San Diego, CA [Abstract]
In this talk, we focus on two puzzles coming from two lines of research. First, cortical neurons show high level of spontaneous activity. The role of this metabologically expensive and richly structured ongoing neural signal with strong stimulus independent variance is presently unknown. Second, previous theoretical approaches proposed that neural activity in the primary visual […]
Popovic M., Lengyel M. & Fiser J. (2012) Decision-making under time constraints supports sampling-based representation of uncertainty in vision. ECVP 2012, Perception 41, 58-59 [Abstract]
Increasing body of psychophysical evidence supports the view of human perception as probabilistic inference that relies on representations of uncertainty about sensory stimuli and that is appropriate for statistically optimal decision making and learning. A recent proposal concerning the neural bases of these representations posits that instantaneous neural activity corresponds to samples from the probability […]
Berkes P., Turner R. & Fiser J. (2011) The army of one (sample): the characteristics of sampling-based probabilistic neural representations. COSYNE 2011, Nature Precedings (2011) [Abstract]
There is growing evidence that humans and animals represent the uncertainty associated with sensory stimuli and utilize this uncertainty during planning and decision making in a statistically optimal way. Recently, a nonparametric framework for representing probabilistic information has been proposed whereby neural activity encodes samples from the distribution over external variables. Although such sample-based probabilistic […]
Berkes P., Orbán G., Lengyel M. & Fiser J. (2009) Matching spontaneous and evoked activity in v1: a hallmark of probabilistic inference. COSYNE 2009, Frontiers in Systems Neuroscience Conference Abstract: Computational and systems neuroscience [Abstract]
Neural responses in the visual cortex of awake animals are highly variable, display substantial spontaneous activity even when no visual stimuli are being processed, and the variability in both evoked activity (EA) and spontaneous activity (SA) is strongly structured. However, most theories of visual cortical function remain mute about the possible computational roles and consequences […]
Orbán G., Lengyel M. & Fiser J. (2007) V1 activity as optimal Bayesian inference. COSYNE 2007, Salt Lake City, UT [Abstract]
Previous theoretical approaches relating neural activity in the primary visual cortex to formal computational goals that V1 might be optimized for focused mostly on the filter-like properties of cells or on related features such as contrast invariance. The key insights gained from these studies were that V1 neurons (simple cells, in particular) can be seen […]
Fiser J., Orbán G., Berkes P. & Lengyel M. (2012) Explaining neural variability in the visual cortex through sampling-based neural representations. AREADNE 2012: Research in Encoding and Decoding of Neural Ensembles, Santorini, Greece [Abstract]
It is well-documented that neural responses in sensory cortices are highly variable: the same stimulus can evoke a different response on each presentation. Traditionally, this variability has been considered as noise and eliminated by using trial-averaged responses. Such averaged responses have been used almost exclusively for characterizing neural responses and mapping receptive fields with tuning […]
Turner RE., Berkes P. & Fiser J. (2011) Learning complex tasks with probabilistic population codes. COSYNE 2011, Nature Precedings (2011) [Abstract]
Recent psychophysical experiments imply that the brain employs a neural representation of the uncertainty in sensory stimuli and that probabilistic computations are supported by the cortex. Several candidate neural codes for uncertainty have been posited including Probabilistic Population Codes (PPCs). PPCs support various versions of probabilistic inference and marginalisation in a neurally plausible manner. However, […]
MacKenzie K. & Fiser J. (2008) Sensitivity of implicit visual rule-learning to the saliency of the stimuli. VSS 2008, Journal of Vision 8 (6), 474-474 [Abstract]
Human infants have been shown to implicitly learn rules, such as the repetition of ABB or ABA patterns, regardless of the identity of the participating items, both with sequential information during language development and with simultaneously presented visual patterns. However, in these studies the ABB or ABA patterns were defined by the identity of the […]
Fiser J., Bourjaily M., Chiu C. & Weliky M. (2006) Mapping different states in neural activity in the primary visual cortex of the awake ferret. SFN 2006, Atlanta, GA [Abstract]
There is a discrepancy between the generally accepted role of ongoing activity during visual development, where spontaneous firing is viewed as an important guiding activity indispensable for proper emergence of the visual structure, and during visual perception, where spontaneous neural activity is considered to be unwanted noise. This discrepancy stems from the presently dominant view […]
MacKenzie KJ., McDevitt EA., Fiser J. & Mednick SC. (2012) The differing effects of REM and non-REM sleep on performance in visual statistical learning. VSS 2012, Journal of Vision 12 (9), 283-283 [Abstract]
Although visual statistical learning (VSL) has been established as a method for testing implicit knowledge gained through observation, little is known about the mechanisms underlying this type of learning. We examined the role of sleep in stabilization and consolidation of learning in a typical VSL task, where subjects observed scenes composed of simple shape combinations […]